Papers
Trivializations for Gradient-Based Optimization on Manifolds
Mario Lezcano Casado
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur et al.
Generalization Error Analysis of Quantized Compressive Learning
Xiaoyun Li, Ping Li
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
Guy Lorberbom, Andreea Gane, Tommi Jaakkola et al.
Zero-shot Learning via Simultaneous Generating and Learning
Hyeonwoo Yu, Beomhee Lee
Hindsight Credit Assignment
Anna Harutyunyan, Will Dabney, Thomas Mesnard et al.
Large Scale Adversarial Representation Learning
Jeff Donahue, Karen Simonyan
Learning Bayesian Networks with Low Rank Conditional Probability Tables
Adarsh Barik, Jean Honorio
Metalearned Neural Memory
Tsendsuren Munkhdalai, Alessandro Sordoni, TONG WANG et al.
Kernel Instrumental Variable Regression
Rahul Singh, Maneesh Sahani, Arthur Gretton
Distributionally Robust Optimization and Generalization in Kernel Methods
Matthew Staib, Stefanie Jegelka
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Zhiqiang Tang et al.
Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi et al.
Compositional De-Attention Networks
Yi Tay, Anh Tuan Luu, Aston Zhang et al.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression
JIAJIN LI, SEN HUANG, Anthony Man-Cho So
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Motonobu Kanagawa, Philipp Hennig
Learning-Based Low-Rank Approximations
Piotr Indyk, Ali Vakilian, Yang Yuan
RUDDER: Return Decomposition for Delayed Rewards
Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich et al.
q-means: A quantum algorithm for unsupervised machine learning
Iordanis Kerenidis, Jonas Landman, Alessandro Luongo et al.
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song et al.
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
Natasa Tagasovska, Damien Ackerer, Thibault Vatter
Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett, Nathan Kallus, Tobias Schnabel
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Daniel Freeman, David Ha, Luke Metz
Visualizing and Measuring the Geometry of BERT
Emily Reif, Ann Yuan, Martin Wattenberg et al.